基于多尺度局部区域置信度传播算法的图像分割

针对基于多尺度马尔可夫随机场(Markov random fields,MRF)的图像分割中常产生块效应的问题,提出了一种多尺度置信度传播(belief propagation)算法,通过建立不同尺度的局部区域,在MRF分割模型上进行区域消息的传播,最终基于局部区域概率的最大后验准则(maximum a posterior)得到图像的分割结果.提出的算法把图像的局部区域特征和全局特征结合起来,在图像的精细层进行多尺度消息的传递,避免了常规多尺度MRF模型层间误分类的传递.提出的算法不仅得到了更准确的图像分割结果,而且具有较快的分割速度.实验结果表明了提出算法的有效性.

Image Segmentation Based on Multiscale Local Region Belief Propagation Algorithm

Image segmentation approaches based on the conventional multiresolution Markov random field( MRF) often produced blocky artifacts. To solve this problem,a new multiscale local region belief propagation( BP) algorithm was proposed. This algorithm based on MRF model built local region messages with different scales,then the messages were propagated on MRF,and segmentation results were finally estimated by local region probabilities based on maximum a posterior( MAP) criterion. This algorithm combined local region features with global features,and multiscale messages were propagated on the finest MRF,which avoided misclassified result propagating between levels on the conventional multiresolution MRF model. Therefore, the proposed algorithm obtained not only more accurate segmentation results but also faster speeds. Experimental results on a wide variety of images had verified the effectiveness of this algorithm.